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检索条件"任意字段=8th IFIP International Conference on Engineering for Human-Computer Interaction"

共 2033 条记录，以下是41-50 订阅

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the application of deep learning in bridge surface disease detection 8

The application of deep learning in bridge surface disease d...

引用

8th international conference on Electrical, Mechanical and computer engineering, ICEMCE 2024

作者： Hu, Yunchuan Wan, Fang Zheng, Zhaoxia Lei, Guangbo Hubei University of Technology School of Computer Science Wuhan China

ISBN: (纸本)9798331506230

To improve the efficiency and accuracy of bridge damage target detection, this paper proposes an improved YOLOv8 detection algorithm, KAC-YOLO, which integrates the KAN network, Attention-based Intra-Feature interaction in Scale (AIFI), and the SIoU loss function. the study enhances feature extraction through the KAN network and improves sensitivity to multi-scale damage features by utilizing the AIFI mechanism for cross-scale feature interaction. Additionally, the SIoU loss function is applied to accelerate model convergence. From a system design perspective, the architecture and modules were optimized to ensure detection efficiency and accuracy. Experimental results show that the improved YOLOv8 model significantly boosts average accuracy and detection speed on the bridge damage dataset. Compared with the original model, KAC-YOLO not only improves detection performance but also demonstrates better adaptability and robustness, providing an efficient and precise solution for bridge structural health monitoring. © 2024 IEEE.

关键词： Damage detection

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An Interdisciplinary Approach to Enhancing computer Privacy and Security: A Synergistic Legal and Technological Governance Framework 24

An Interdisciplinary Approach to Enhancing Computer Privacy ...

引用

8th international conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Zheng, Yulin Lu, Xinghua Zhu, Ying Xiang, Cailiang Tang, Yanfeng Hu, Jianxin Guangzhou Huali College Guangdong Guangzhou China Guangdong Nanhua Vocational College of Industry and Commerce Guangdong Guangzhou China

ISBN: (纸本)9798400710094

this paper is dedicated to exploring interdisciplinary and collaborative governance strategies for computer privacy and security, and proposes a collaborative governance framework that integrates law and technology to address the ever-increasing challenges of cybersecurity and privacy protection. the study first analyses the current state of computer privacy and security, reveals the interaction between legal frameworks and technological tools and their limitations, and highlights their complementary roles in combating emerging threats. the paper analyses the theoretical foundations of the synergy between law and technology, and attempts to construct a comprehensive synergistic governance framework aimed at balancing the dynamic relationship between privacy protection and technological innovation. © 2024 Copyright held by the owner/author(s).

关键词： Collaborative Governance

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8th international conference on Software engineering and computer Systems, ICSECS 2023

8th International Conference on Software Engineering and Com...

引用

8th IEEE international conference on Software engineering and computer Systems, ICSECS 2023

ISBN: (纸本)9798350310931

the proceedings contain 87 papers. the topics discussed include: designing an e-voting framework using blockchain: a secure and transparent attendance approach;SODIBOT: a two in one real-time human monitoring system using YOLO algorithms;priority based fair scheduling: enhancing efficiency in cloud job distribution;a survey on supervised machine learning in intrusion detection systems for Internet of things;machine learning regression models for real-time touchless interaction applications;knowledge of the utilization of telegram for learning among primary students in Kuantan;exploring machine learning in IoT smart home automation;predicting mental health disorder on twitter using machine learning techniques;improving sarcasm detection in mash-up language through hybrid pretrained word embedding;and an overview of part-of-speech tagging methods and datasets for Malay language.

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Deep Learning based Recognition of Auto Covariance Signatures through Protein interaction Model for Amino Acid Sequence 8

Deep Learning based Recognition of Auto Covariance Signature...

引用

8th international conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Balamurugan, K.S. Geetha Rani, K. Sadish Sendil, M. Kumutha, D. Surendran, R. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India Department of Computer Science and Engineering Bangalore560069 India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Vel Nagar Avadi Chennai India Jeppiaar Institute of Technology Sriperumudur Department of Electronics and Communication Engg Tamilnadu India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798350367904

the detection of protein interaction partners using amino acid sequences is a challenging task in bioinformatics and molecular biology. this study proposes a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to improve the accuracy of protein interaction prediction. Auto-covariance signatures are employed to capture the positional relationships between amino acids and protein sequences, facilitating the extraction of structural and functional features. CNNs are used to automatically extract both short-and long-range dependencies, while RNNs handle temporal patterns. the model incorporates self-supervised learning to handle unlabeled data, improving generalization. With a benchmark dataset, the system achieved an impressive accuracy of 98.9%, outperforming existing methods in protein interaction prediction. the approach also highlights the importance of optimization techniques in managing protein sequence structures and functions effectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploration and Optimization Technique for Speech Enhancement and Emotion Recognition Using Deep Neural Network 8

Exploration and Optimization Technique for Speech Enhancemen...

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8th international conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Dere, Yash Botre, Prasad Telgar, Audumbar Paikrao, Pavan Kumar D. Dr D Y Patil Institute of Technology Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798331520762

One of the most fascinating and difficult tasks in human-computer interaction is speech emotion recognition. the practice of attempting to determine the types of affective and emotional states in speech is known as speech emotion recognition. this takes advantage of the fact that voice pitch and tone often reveal underlying emotion. Animals like horses and dogs also use this phenomenon to understand human emotion. We attempted to identify emotion in a brief voice communication in this experiment. Four datasets-SAVEE, CREMAD, IEMOCAP, TESS, and RAVDESS-that comprise about seven primary emotion types-Happy, Fear, Angry, Disgust, Surprised, Sad, or Neutral-were employed in this effort. In contrast to earlier research, where each researcher worked on a different dataset, our project integrated the four datasets-SAVEE, RAVDESS, TESS, and CREMA-D into a single file, which we then sent as input to the model in the form of a wav file. After removing the noise from the data using feature extraction techniques (MFCC, ZCR, and RMSE), we organized the sequential data in a 3D array format that the CNN model can use. the average accuracy of the model is 76% during the testing phase and 87% during the training phase, according to our repeated testing with different values after putting the data into a graphical format using the Matplotlib tool. © 2025 IEEE.

关键词： Deep neural networks

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Evolving GAN-BERT Architecture for Efficient Text Categorization with Minimal Labeled Data 8th

Evolving GAN-BERT Architecture for Efficient Text Categoriza...

引用

8th ifip TC 12 international conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Bagavathi, C. Raakesh, M. Bhanu, Chanamala Vipasyana Reddy, Katheram Venkata Sai Ram Sathwik, Kancharla Rupesh Sai Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9783031736162

Categorical text generation is an upcoming field within natural language processing focusing on producing text tailored to specific categories or domains. the goal of categorical text production is to produce text that complies with the requirements and characteristics of the chosen category while also adhering to the grammatical and structural conventions of the language. Text classification works across multiple data categories, and the synthetic data generated usually adheres to a preset pattern and ignores the text’s nuanced emotional content. the proposed approach begins with using BERT to get the text’s deep contextual embeddings. Using these text embeddings, the underlying semantics of the target category can be better understood by adding a task-specific layer that places the sentences in the closest corresponding class. After that, the architecture and settings of the GAN are optimized through evolutionary computing techniques to guarantee that the generated text conforms to the constraints and characteristics specific to each category. © ifip international Federation for Information Processing 2025.

关键词： Generative adversarial networks

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Explaining Sentiments in Indian Legal Judgments with LIME and SHAP 8th

Explaining Sentiments in Indian Legal Judgments with LIME an...

引用

8th ifip TC 12 international conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Babu, T. Hemanth Kiran, V. Gopi Bhat, Suhas S. Prabhakar, Priyanka Pati, Peeta Basa Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031736162

this paper presents a comparative study on sentiment analysis applied to legal texts, specifically legal opinions, and judgments. this paper uses embeddings such as T5, XLM Roberta and BERT, Roberta, and Legal BERT to convert the textual data into pre-trained language model word representation, to make it easier for ML algorithms to determine the sentiment (positive, negative, or neutral) of legal documents. the dataset was converted into embeddings, and various models, including KNN, ANN, SVM, random forest, XG boost, logistic regression, and decision tree, were employed for sentiment analysis. Each model was subjected to hyperparameter tuning using Grid Search CV, resulting in the highest accuracy of 66.66% with SVM on T5 embeddings. this research provides valuable insights into the emotional degrees within legal texts, demonstrating the effectiveness of our sentiment analysis approach. © ifip international Federation for Information Processing 2025.

关键词： Embeddings

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Examine and Assess the Influence of Content Development on Xiaohongshu Platform for Brand interaction: An In-depth Analysis of a Minor Stationery Corporation's Strategy 10

Examine and Assess the Influence of Content Development on X...

引用

10th international conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Ou, Ting Tiamkaew, Ekkasit Kornsurin, Phusit Santirojanakul, Sirikorn Chiang Mai University College of Arts Media and Technology Chiang Mai Thailand Naresuan University Department of Computer Science and Information Technology Phitsanulok Thailand Payap University Faculty of Business Administration Chiang Mai Thailand

ISBN: (纸本)9798331543273

this paper takes the small stationery brand of XiaoHongshu platform as the research object, and discusses the influence of its content marketing on brand communication. through the combination of quantitative and qualitative methods, 400 "notes"from 20 leading small stationery brands were analyzed, considering external factors such as release timing, market competition, and product popularity, and the characteristics of high-quality and low-quality notes and their communication effects were summarized. the study also takes into account social behavior factors, such as the use of Key Opinion Leaders (KOLs). these factors can significantly influence user engagement and brand perception. the study found that high-quality notes are generally outstanding in cover design, title relevance, content graphic combination and hashtag use, and are more inclined to use video (62% of high-quality notes), and significantly improve user interaction (average engagement volume 19,763). In addition, "Product display"and "product details introduction"accounted for the highest proportion of topics in notes, at 42.5% and 36%, respectively. Statistical analysis yielded ?2= 67.5, p2= 67.5 shows that there is a significant difference between video notes and graphic notes in introducing product details. p © 2025 IEEE.

关键词： Strategic planning

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interaction Techniques for Remote Maintenance in an AR Shared Environment 10th

Interaction Techniques for Remote Maintenance in an AR Share...

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10th ifip WG 13.2 international Working conference on human-Centered Software engineering (HCSE)

作者： Krings, Sarah Claudia Biermeier, Kai Yigitbas, Enes Paderborn Univ Warburger St 100 D-33098 Paderborn Germany

ISBN: (纸本)9783031645754;9783031645761

In today's world, the importance of remote collaboration for maintenance scenarios is strongly increasing. Especially in the industrial context where machinery and other objects have to be maintained, traditional collaboration tools such as video conferencing systems are not helpful enough to communicate and interact about situation-specific spatial information. In this work, we identify and discuss current challenges in enhancing communication and interaction for mobile augmented reality (AR) based collaboration approaches. To address the identified challenges we introduce a mobile AR solution that integrates various features like voice communication, text chat, screen share, AR annotations, and AR animations to enhance the task of remote collaboration between an on-site technician and a remote expert. Based on a coffee machine maintenance example scenario we have conducted a user study and compared the benefits and drawbacks of different interaction techniques in our mobile AR collaboration solution.

关键词： mixed reality collaboration augmented reality virtual reality maintenance usability

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Swarm Intelligence-Based Feature Selection Algorithm of EEG Classification for Brain Emotion Detection: A Review 8

Swarm Intelligence-Based Feature Selection Algorithm of EEG ...

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8th IEEE international conference for Convergence in Technology, I2CT 2023

作者： Kaur, Gaganjot Gupta, Meenu Kumar, Rakesh Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350334012

In recent technology, Brain computer Interfacing (BCI) plays a prominent role through which a human can interact with the outside world using brain signals. Brain signals are the current reactions in the brain when neurological activity occurs and are closely associated with critical thinking, logical decision-making, recognition, human interaction, and to some extent, human Intelligence. Brain waves influence most real human body reactions;compared to other types of emotion analysis, brain wave emotion analysis comes out on top. Emotion Detection has a broad perspective in interdisciplinary research for neurologists, psychologists, engineers, etc. this paper aims to represent the latest comprehensive review of emotion detection techniques using Electroencephalography (EEG) Signals, feature extraction, feature selection, and classification, along with pointing out existing problems in the field and potential growth areas. © 2023 IEEE.

关键词： Brain

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